Understanding the language of proteins with
deep learning

Level: Medium

By Marie Lopez, Mohamed Mounir Moussa , Ibtissem Kadri

Overview:

With the increasing availability of large-scale biological data, the
applications of deep learning approaches are now expanding to several fields of
healthcare and life sciences. The promise of tackling problems that could
benefit the health of millions of people together with the necessity to analyse
highly dimensional and highly structured data, have led to key developments in
machine learning applied to diagnostic imaging, disease severity prediction and
proteomics. As proteins are the machinery of life, one of the crucial challenges
for computational biology is to be able to predict the function and 3D structure
of proteins from their sequences of amino-acids alone. Recently, such
predictions have been made possible by the use of language modeling
self-supervised trainings and the use of state-of-the-art architecture like the
transformer, to help closing the gap of sequence annotation. In this workshop,
we will show participants how to use protein pre-trained embeddings to build a
classifier of African Covid-19 strains and predict their respective 3D
structures for comparison. We will provide the necessary guidance, tools and
background to understand the key concepts of biology, how to use protein
pre-trained embeddings and how to predict protein 3D structures from scratch
using Alphafold2.

Outcome:

At the end of the workshop, participants should have a good understanding of how
to use pre-trained embeddings and de novo structure predictions to help protein
characterisation as well as knowledge about real-life use-cases. On the
practical side, participants:
Should understand the differences between several protein embeddings and be able
to compute them for any protein sequence.
Are able to perform protein 3D structure predictions and compare them with the
appropriate tools.

Prerequisites:

Participants should be comfortable with programming in Python.
Have a basic understanding of key concepts in machine learning and in
particular, natural language processing.
No prior knowledge of biology is expected or required.

Machine learning with Google Cloud Platform

Level: Beginner

By Mouafek Ayadi

Qwiklabs Workshop Description:

Qwiklabs Workshop Description
Build a simple End-to-End Machine Learning solution for Predicting Housing
Prices using Tensorflow and AI Platform and leverage the Cloud for distributed
training and online prediction.
Sources

Sources & Prerequisites:

• • https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/blogs/housing_prices/cloud-ml-housing-prices.ipynb
    

• https://www.qwiklabs.com/focuses/3644?catalog_rank=%7B%22rank%22%3A2%2C%22num_filters%22%3A1%2C%22has_search%22%3Atrue%7D&parent=catalog&search_id=13799566
  

Machine Learning for Hackers and Slackers – the Workshop

Level: Intermediate

By Amanda Minnich & Will Pearce

Description :

Learn the basics of assessing the security of your ML modals with Azure/counterfit:
a CLI that provides a generic automation layer for assessing the security of ML models.

Check the project on github for more information:

https://github.com/Azure/counterfit

Introductory PyTorch resources

Level: Beginner

By Daniel Godoy

Overview :

Learn the basics of building a PyTorch model using a structured, incremental and from first
principles approach. Find out why PyTorch is the fastest growing Deep Learning framework and
how to make use of its capabilities: autograd, dynamic computation graph, model classes,
data loaders and more.
The main goal of this session is to show you how PyTorch works: we will start with a simple
and familiar example in Numpy and "torch" it! At the end of it, you should be able to
understand PyTorch's key components and how to assemble them together into a working model.

Resources & Prerequisites:

We will use Google Colab and work our way together into building a complete model in
PyTorch. You should be comfortable using Jupyter notebooks, Numpy and, preferably, object
oriented programming.

https://github.com/dvgodoy/FundamentalsPyTorch_IndabaX_Tunisia